Conventional source identification methods work with a Gaussian plume dispersion Model. See, for example, Kiemle et al., “Potential of Spaceborne Lidar Measurements of Carbon Dioxide and Methane Emissions from Strong Point Sources,” Remote Sensing, 2017, 9, 1137 (November 2017) (16 total pages). The Gaussian plume model however needs quasi steady state assumption or wind conditions that are fixed in space and for a sufficient length of time for the Gaussian plume to develop from averaging of stochastic instantaneous plume trajectories.
Further, while the Gaussian plume model has been successfully used in atmospheric pollution dispersion over longer length and time scales, the Gaussian plume is an idealized condition and may not be applicable at shorter time and length scales.
These drawbacks have led to the development of alternative analytics for source identification. These techniques however still require knowledge of a plume dispersion model to localize the source. At short length scales (e.g., less than 50 meters) typical Gaussian plume dispersion models are unreliable and plume characteristics have upwind migration.
Therefore, improved source identification techniques would be desirable.